Difference between revisions of "Team:Heidelberg/Model"

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<h3>★  ALERT! </h3>
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<p>This page is used by the judges to evaluate your team for the <a href="https://2017.igem.org/Judging/Medals">medal criterion</a> or <a href="https://2017.igem.org/Judging/Awards"> award listed above</a>. </p>
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<p> Delete this box in order to be evaluated for this medal criterion and/or award. See more information at <a href="https://2017.igem.org/Judging/Pages_for_Awards"> Instructions for Pages for awards</a>.</p>
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    Modeling|
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    Overview|
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    {{Heidelberg/abstract|https://static.igem.org/mediawiki/2017/a/ad/T--Heidelberg--Team_Heidelberg_2017_modeling_graphical_abstract.jpg|
<h1> Modeling</h1>
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        Successful <i>in vivo</i> directed evolution by PREDCEL and PACE requires the thorough consideration of experimental parameters, e.g. phage propagation times, culture dilution rates and inducer/inhibitor concentrations. We employed extensive ODE-based and stochastic modeling to identify the most sensitive parameters and adapt our experiments accordingly. First, we calibrated our models using phage propagation experiments from our wet lab complemented with literature data. Simulations showed that the phage titer is highly sensitive to culture dilution rates. We simulated batch times and transfer volumes for PREDCEL and corresponding flow rates for PACE to determine optimized conditions for gene pool selection while avoiding phage washout. We also estimated phage titer monitoring intervals for cost and labor efficient QC/monitoring as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases. Finally, we provide a web-based, fully interactive modeling platform that not only informed our wet lab experiments, but enables future iGEM teams to efficiently build on our work.
 
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
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                {{Heidelberg/panelelement|Phage titer|https://static.igem.org/mediawiki/2017/2/2b/T--Heidelberg--2017_phage-titer-logo.png|https://2017.igem.org/Team:Heidelberg/Model/Phage_Titer|
 
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                Simulations of phage and <i>E. coli</i> titer support both PREDCEL and PACE by helping to choose a set of experimental parameters that is both efficient in terms of directed evolution and in terms of usability.|Numeric Model
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<h3> Gold Medal Criterion #3</h3>
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                {{Heidelberg/panelelement|Interactive Webtools|https://static.igem.org/mediawiki/2017/e/e3/T--Heidelberg--2017_interactive_tools-logo.png|https://2017.igem.org/Team:Heidelberg/Model/Tools|
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                Use the interactive tools to simulate the conditions you are interested in and explore how the combined experimental parameters influence experimental outcomes.|Overview
To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.  
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                {{Heidelberg/panelelement|Mutagenesis Induction|https://static.igem.org/mediawiki/2017/4/48/T--Heidelberg--2017_mutagenesis-induction-logo.png|https://2017.igem.org/Team:Heidelberg/Model/Arabinose|
 
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                {{#tag:html|Model the glucose and arabinose concentration to make sure mutagenesis plasmids are sufficiently induced to get optimal mutagenesis conditions for both PREDCEL and PACE.</p><a href="https://2017.igem.org/Team:Heidelberg/Model/Mutagenesis_Induction" class="card-button">Analytic Model</a></p><p style="text-align: center !important;"><a href="https://2017.igem.org/Team:Heidelberg/Model/Glucose" class="card-button">Glucose Tool</a><p>}}|Arabinose Tool
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Please see the <a href="https://2017.igem.org/Judging/Medals"> 2017 Medals Page</a> for more information.
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                {{Heidelberg/panelelement|Lagoon Contamination|https://static.igem.org/mediawiki/2017/8/8e/T--Heidelberg--2017_lagoon_contamination-logo.png|https://2017.igem.org/Team:Heidelberg/Model/Contamination|
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                {{#tag:html|Check if lagoons are vulnerable to contamination by microorganisms under given experimental conditions.</p><a href="https://2017.igem.org/Team:Heidelberg/Model/Lagoon_Contamination" class="card-button">Analytic Model</a><p>}}|Interactive Webtool
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                {{Heidelberg/panelelement|Mutation Rate Estimation|https://static.igem.org/mediawiki/2017/a/ab/T--Heidelberg--2017_mutation_rate_estimation-logo.png|https://2017.igem.org/Team:Heidelberg/Model/Mutation|
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                {{#tag:html|Estimate the number of mutated sequences in a PREDCEL or PACE experiment at a given point in time to check for the covered sequence space and to save time and money when sequencing.</p><a href="https://2017.igem.org/Team:Heidelberg/Model/Mutation_Rate_Estimation" class="card-button">Analytic Model</a><p>}}|Interactive Webtool
<h3>Best Model Special Prize</h3>
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                {{Heidelberg/panelelement|Medium Consumption|https://static.igem.org/mediawiki/2017/1/13/T--Heidelberg--2017_medium_consumption-logo.png|https://2017.igem.org/Team:Heidelberg/Model/Medium|
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                {{#tag:html|Calculate the amount of medium needed for a PACE experiment, see how medium consumption can be reduced when experimental parameters are optimized.</p><a href="https://2017.igem.org/Team:Heidelberg/Model/Medium_Consumption" class="card-button">Analytic Model</a><p>}}|Interactive Webtool}}
To compete for the <a href="https://2017.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.  
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                {{Heidelberg/panelelement|Equilibration MD simulations|https://static.igem.org/mediawiki/2017/3/38/T--Heidelberg--2017_GUS_PREPARATION_FRAGMENTS.svg|https://2017.igem.org/Team:Heidelberg/Validation|
You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
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                To assert what effects our mutations entail on protein fold, we performed Molecular Dynamics simulations.|Software Validation
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<h5> Inspiration </h5>
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Here are a few examples from previous teams:
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<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</a></li>
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<li><a href="https://2016.igem.org/Team:TU_Delft/Model">TU Delft 2016  </li>
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
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Latest revision as of 16:34, 1 November 2017


Modeling
Overview
Successful in vivo directed evolution by PREDCEL and PACE requires the thorough consideration of experimental parameters, e.g. phage propagation times, culture dilution rates and inducer/inhibitor concentrations. We employed extensive ODE-based and stochastic modeling to identify the most sensitive parameters and adapt our experiments accordingly. First, we calibrated our models using phage propagation experiments from our wet lab complemented with literature data. Simulations showed that the phage titer is highly sensitive to culture dilution rates. We simulated batch times and transfer volumes for PREDCEL and corresponding flow rates for PACE to determine optimized conditions for gene pool selection while avoiding phage washout. We also estimated phage titer monitoring intervals for cost and labor efficient QC/monitoring as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases. Finally, we provide a web-based, fully interactive modeling platform that not only informed our wet lab experiments, but enables future iGEM teams to efficiently build on our work.
Card image cap

Phage titer

Simulations of phage and E. coli titer support both PREDCEL and PACE by helping to choose a set of experimental parameters that is both efficient in terms of directed evolution and in terms of usability.

Card image cap

Interactive Webtools

Use the interactive tools to simulate the conditions you are interested in and explore how the combined experimental parameters influence experimental outcomes.

Card image cap

Mutagenesis Induction

Model the glucose and arabinose concentration to make sure mutagenesis plasmids are sufficiently induced to get optimal mutagenesis conditions for both PREDCEL and PACE.

Analytic Model

Glucose Tool

Card image cap

Lagoon Contamination

Check if lagoons are vulnerable to contamination by microorganisms under given experimental conditions.

Analytic Model

Card image cap

Mutation Rate Estimation

Estimate the number of mutated sequences in a PREDCEL or PACE experiment at a given point in time to check for the covered sequence space and to save time and money when sequencing.

Analytic Model

Card image cap

Medium Consumption

Calculate the amount of medium needed for a PACE experiment, see how medium consumption can be reduced when experimental parameters are optimized.

Analytic Model

Card image cap

Equilibration MD simulations

To assert what effects our mutations entail on protein fold, we performed Molecular Dynamics simulations.

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